Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek AI's first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we demonstrate how to get begun with DeepSeek-R1 on Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the designs as well.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language design (LLM) developed by DeepSeek AI that uses support discovering to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) action, which was used to fine-tune the design's responses beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's equipped to break down intricate questions and reason through them in a detailed way. This assisted reasoning process enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be integrated into various workflows such as agents, sensible thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, making it possible for effective reasoning by routing queries to the most appropriate professional "clusters." This technique allows the model to concentrate on different issue domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.

DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to mimic the behavior and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and assess models against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative AI applications.

Prerequisites

To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation increase, develop a limitation increase request and reach out to your account team.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up approvals to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and examine designs against essential safety criteria. You can implement security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.

The general circulation includes the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:

1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. 2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.

The model detail page supplies essential details about the design's abilities, pricing structure, and implementation guidelines. You can discover detailed use instructions, including sample API calls and code snippets for combination. The model supports various text generation tasks, including material development, code generation, and question answering, utilizing its support discovering optimization and CoT thinking abilities. The page also consists of release options and licensing details to help you begin with DeepSeek-R1 in your applications. 3. To begin using DeepSeek-R1, choose Deploy.

You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. 4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). 5. For Number of instances, go into a variety of circumstances (in between 1-100). 6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you might want to examine these settings to line up with your company's security and compliance requirements. 7. Choose Deploy to start utilizing the design.

When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. 8. Choose Open in play area to access an interactive interface where you can experiment with different triggers and adjust design criteria like temperature level and optimum length. When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for inference.

This is an excellent method to check out the design's thinking and text generation abilities before integrating it into your applications. The play ground provides immediate feedback, helping you understand how the design responds to different inputs and letting you fine-tune your triggers for optimum results.

You can quickly evaluate the design in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a demand to generate text based on a user prompt.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the approach that best suits your requirements.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, choose Studio in the navigation pane. 2. First-time users will be triggered to create a domain. 3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The model browser shows available designs, with details like the provider name and model capabilities.

4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. Each design card shows essential details, including:

- Model name

• Provider name
• Task category (for example, Text Generation). Bedrock Ready badge (if relevant), showing that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
  
  5. Choose the design card to view the design details page.
  
  The model details page consists of the following details:
  
  - The model name and company details. Deploy button to release the model. About and Notebooks tabs with detailed details
  
  The About tab consists of crucial details, such as:
  
  - Model description.
• License details.
• Technical specs.
• Usage standards
  
  Before you release the model, it's advised to evaluate the design details and license terms to confirm compatibility with your use case.
  
  6. Choose Deploy to continue with deployment.
  
  7. For Endpoint name, use the immediately produced name or develop a custom-made one.

8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the number of instances (default: 1). Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the model.
    
    The deployment process can take a number of minutes to finish.
    
    When release is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can conjure up the design using a SageMaker runtime customer and integrate it with your applications.
    
    Deploy DeepSeek-R1 using the SageMaker Python SDK
    
    To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
    
    You can run additional demands against the predictor:
    
    Implement guardrails and run reasoning with your SageMaker JumpStart predictor
    
    Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:
    
    Tidy up
    
    To prevent unwanted charges, complete the actions in this area to clean up your resources.
    
    Delete the Amazon Bedrock Marketplace release
    
    If you released the design using Amazon Bedrock Marketplace, total the following steps:
    
    1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations.
12. In the Managed implementations section, locate the endpoint you wish to delete.
13. Select the endpoint, and on the Actions menu, select Delete.
14. Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name.
15. Model name.
16. Endpoint status
    
    Delete the SageMaker JumpStart predictor
    
    The SageMaker JumpStart model you released will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.
    
    Conclusion
    
    In this post, we checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
    
    About the Authors
    
    Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI business construct innovative solutions utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek takes pleasure in hiking, enjoying movies, and trying different foods.
    
    Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
    
    Jonathan Evans is a Specialist Solutions Architect dealing with generative AI with the Third-Party Model Science group at AWS.
    
    Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, wakewiki.de SageMaker's artificial intelligence and generative AI hub. She is passionate about constructing options that assist consumers accelerate their AI journey and unlock organization worth.